Business predictions 2026: AI will become a bigger part of manufacturing operations

Future technology. Image by Tim Sandle

What are the key business trends for 2026? In particular, how will firms make use of AI and what impact will AI have on the way that firms handle data and their own internal organisational structure?

To provide some answers, Digital Journal asked experts from the executive team at the company Configit for their predictions and trends.

Prediction 1: AI will become a bigger part of manufacturing operations, including configuration and knowledge capture 

According to Sigrún Ívarsdóttir, product manager: “AI is poised to continue transforming the manufacturing industry in the coming year in many different ways. For one thing, I expect we’ll see AI become more embedded across design and production systems, especially in connecting systems like PLM and ERP/Sales (for configuration data) to reduce silos and speed up product decisions. Additionally, configuration will become more predictive, with AI and integrations helping identify configurations that are both market-relevant and feasible to produce.”

Prediction 2: 2026 will be a race to capture institutional knowledge 

On the subject of workplace demographics and knowledge capture, Ívarsdóttir says: “With an ageing workforce, manufacturers face a significant challenge in transferring knowledge. Capturing and digitizing configuration and engineering knowledge will be essential in order for manufacturers to stay competitive. In 2026, we expect to see acceleration of efforts to digitize this tribal knowledge. AI can play a key in preserving and surfacing the expertise of veteran workers.”

Prediction 3:  Cross-divisional alignment will take hold as customer expectations grow 

To meet customer expectations, firms will need to undergo internal realignment. Here Ívarsdóttir finds: “A big shift will come from tighter integration between PLM and ERP/Sales, ensuring engineering, manufacturing, sales and service all work from a shared source of truth. This will be an important shift as customers continue to expect more personalization with shorter lead times, which puts more pressure on aligning what’s offered commercially with what can actually be built.”

Prediction 4: Data interoperability will be a competitive advantage 

With data handling, Ívarsdóttir thinks: “Data interoperability will become a real differentiator, as manufacturers realise that aligning systems across the product lifecycle is key to scaling AI effectively.”

Prediction 5: Reliable data foundations will become the new competitive edge 

To support AI development, reliable and good quality data is required. This is the opinion of Daniel Joseph Barry, vice president of product marketing, who states: “As generative AI captures the attention of C-level executives in the discrete manufacturing sector, there’s massive pressure to become more efficient. AI technologies provide a lot of capabilities that can help manufacturers to achieve their efficiency improvement goals. While this opens manufacturers to new approaches and technologies, it also forces them to look under the hood to understand how their operations, especially their IT systems, are operating today.”

Expanding on this, Barry adds: “What they’re likely to see is a fragmented landscape of data sources that aren’t aligned and are difficult to synchronise, leading to errors and delays. As a result, these leaders will realise that the first step in achieving their efficiency goals through AI is to build a reliable data foundation with connected systems, connected data and connected logic.”

Prediction 6: A redefining of configuration will lead to better outcomes 

The sixth prediction comes from Henrik Hulgaard, co-founder and vice president of product management. Hulgaard adds to the considerations as to how AI will shape the structure of companies, noting: “AI is rapidly transforming configuration processes in manufacturing, both in terms of how models are built and how configurations are executed.”

As examples, Hulgaard states: “It is already having a significant impact on two processes: modelling, where product features and rules are defined, and configuration, where users select compatible options to meet customer requirements. In the coming year, these processes face more rapid change. We believe that the one that has the most impact for our customers, but also the one that is the most challenging, is to find a way to use AI in modelling. Instead of having modelling experts who know how to write rules, AI can take unstructured data such as paragraphs of text or diagrams from documents and automatically translate this information into product models.”

As to the advantages, Hulgaard finds: “This approach has the potential to dramatically reduce the time required to create the configuration models, as well as making it possible for product managers to maintain the information themselves.”

There are other changes as well: “AI will also reshape the configuration process itself. Rather than navigating complex interfaces, users will describe what they need in natural language, and AI will assemble a valid configuration, potentially even coordinating with sub-suppliers’ systems via intelligent agent workflows. Together, these innovations promise faster, smarter, and more collaborative configuration across the manufacturing value chain.”

Prediction 7: AI will drive a new era of efficiency for discrete manufacturing  

A shift away from generic options is being made possible with AI, enabling more effective niche marketing and discrete manufacturing, says Laura Beckwith, director of product management.

Beckwith outlines the expected process: “Discrete manufacturers are already increasingly using AI to optimise configuration processes, and we expect to see this trend in hyperdrive in 2026. AI will enable faster translation of market insights into refined product offerings, simplify engineering through more efficient bill of materials (BOM) management, and accelerate the creation and validation of configuration models. These advances will not only enhance how products are defined and sold but also streamline collaboration across marketing, engineering, and operations. As manufacturers continue shifting from engineer-to-order (ETO) to configure-to-order (CTO) models, AI-powered productivity tools will play a central role, reducing manual effort, shortening lead times, and enabling smarter, data-driven customisation. The result will be more agile, responsive manufacturing ecosystems designed to meet evolving customer needs at scale.”